{"id":"W2946715935","doi":"10.65109/csjp7552","title":"Optimal Risk in Multiagent Blind Tournaments","year":2019,"lang":"en","type":"article","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of Ottawa","funders":"","keywords":"Tournament; Tournament selection; Outcome (game theory); Computer science; Range (aeronautics); Mathematical optimization; Artificial intelligence; Operations research; Selection (genetic algorithm); Mathematics; Mathematical economics; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00129294,0.00004942322,0.00008830237,0.0001220394,0.00005425113,0.00006732072,0.000304133,0.00003083931,0.01174855],"category_scores_gemma":[0.0002294833,0.00003362958,0.00004291215,0.0003792224,0.00002207562,0.0001636798,0.00006199383,0.00009355434,0.01256242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000167822,"about_ca_system_score_gemma":0.00001344918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002175431,"about_ca_topic_score_gemma":0.00001430449,"domain_scores_codex":[0.9989263,0.00008777194,0.0002896519,0.0002337317,0.0003560503,0.0001064921],"domain_scores_gemma":[0.9991474,0.0003032513,0.0001013024,0.0003497507,0.00004612773,0.00005214091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003038359,0.0008262174,0.6003488,0.000001931651,0.00003163185,0.000005661581,0.00252542,0.09230649,0.002710931,0.07464604,0.0201246,0.2061684],"study_design_scores_gemma":[0.005743739,0.0001779537,0.3737117,0.00001482394,0.00001370097,0.00001462913,0.01294415,0.08364457,0.009503806,0.132554,0.3810612,0.0006157499],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9546119,0.00000817842,0.01634176,0.0002969749,0.0001451594,0.0001685888,0.000003652599,0.00001485853,0.02840894],"genre_scores_gemma":[0.966892,0.000005108312,0.002829934,0.0001183626,0.00002207157,0.000009954974,6.230593e-7,0.000002644746,0.03011928],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3609366,"threshold_uncertainty_score":0.9891548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08874053083871841,"score_gpt":0.4091136655080394,"score_spread":0.320373134669321,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}